<title>High-order neural networks for image recognition</title>

Abstract

This paper discusses the application of high-order neural networks (HONNs) for image recognition and image enhancement of digitized images. A key property of neural networks is their ability to recognize invariances and extract essential parameters from complex high- dimensional data. The most significant advantage of the HONN over first-order networks is that invariances to geometric transformations can be incorporated into the network and need not be learned through iterative weight updates. A third-order HONN can be used to achieve translation, scale, and rotation invariant recognition with a significant reduction in training time over other neural net paradigms such as the multilayer perceptron. We have developed a model based on a third-order net that can be trained with various images. Simulation results show that the model is able to perform very well with images embedded in noise. It is also shown that this method outperforms the Hamming net. Our model has also been applied to another difficult and computationally-complex problem: human face recognition. We put forth arguments for the use of isodensity information in the recognition algorithm. A method of image recognition that fuses isodensity information and neural networks is described and its merits over other image recognition methods are expounded. It is shown that isodensity information coupled with the use of an 'adaptive threshold' strategy yields a system that is to a high degree unperturbed by image contrast noise. Simulation results for these applications are presented in the paper.